Preliminary Screening of South African Plants for Binding Affinity to the Serotonin Reuptake Transporter and Adenosine A1/A2A Receptors
Bibliographic record
Abstract
In South African traditional medicine, Gomphocarpus fruticosus (L.) W.T. Aiton, Hypoxis hemerocallidea Fisch. & C.A. Mey., and Leonotis leonurus. (L.) R.Br. have been recorded among different ethnic groups to be a valuable herbal remedy for the management of depression-related conditions. The current study investigated the affinity of these three plants toward the serotonin reuptake transporter (SERT) and adenosine A1/A2 receptors. Six solvents (water, methanol, acetone, dichloromethane, petroleum ether, and hexane) were used to extract the selected plants. We established that eight extracts exerted potential affinity based on the applied in vitro binding experiment. The methanol and acetone extracts of Hypoxis hemerocallidea had 60% specific binding of [3H]citalopram, an indication that almost 40% of the plant extracts were bound to the SERT. For the adenosine receptor binding assays, methanol and hexane extracts of Leonotis leonurus were the most active, with rA1Ki values of 0.038 and 0.176 mg/mL, respectively. In addition, the dichloromethane extract of Gomphocarpus fruticosus had an rA1Ki value of 6.46 mg/mL. Extracts from the more polar solvents methanol and dichloromethane had higher binding affinity. Additionally, these plant extracts acted as antagonists at the adenosine A1 receptor. Overall, the current findings provide an indication of the potential antidepressant effects of some of the tested extracts based on their binding to the receptors evaluated. However, a combination of other in vitro assays is needed to establish possible mechanisms of action. In addition, computational analysis and profiling of plant extracts is crucial to identify the bioactive compounds with a higher affinity to the receptors. Ultimately, in vivo studies remain essential to allow for an in-depth elucidation of the mechanisms of action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".